학술논문

A Tractography-Based Grading Scale of Brain Arteriovenous Malformations Close to the Corticospinal Tract to Predict Motor Outcome After Surgery
Document Type
article
Source
Frontiers in Neurology, Vol 10 (2019)
Subject
arteriovenous malformation
corticospinal tract
patient selection
surgery
tractography
Neurology. Diseases of the nervous system
RC346-429
Language
English
ISSN
1664-2295
Abstract
Background: Surgical decision-making for brain arteriovenous malformations (AVMs) close to the corticospinal tract (CST) is always challenging. The purpose of this study was to develop a tractography-based grading scale to improve preoperative risk prediction and patient selection.Methods: We analyzed a consecutive, surgically treated series of 90 patients with AVMs within a 10-mm range from the CST demonstrated by preoperative diffusion tensor tractography. Poor motor outcome was defined as persistent postoperative limb weakness. We examined the predictive ability of nidus-to-CST distance (NCD), the closest CST level (CCL), deep perforating artery supply, as well as variables of the supplemented Spetzler-Martin grading system. Three logistic models were derived from different multivariable logistic regression analyses, of which the most predictive model was selected to construct a prediction grading scale. Receiver operating characteristic analysis was conducted to test the predictive accuracy of the grading scale.Results: Twenty-one (23.3%) patients experienced persistent postoperative limb weakness after a mean 2.7-year follow-up. The most predictive logistic model showed NCD (P = 0.001), CCL (P = 0.017), patient age (P = 0.004), and AVM diffuseness (P = 0.021) were independent predictors for poor motor outcome. We constructed the CLAD grading scale incorporating these predictors. The predictive accuracy of the CLAD grade was better compared with the supplemented Spetzler-Martin grade (area under curve = 0.84 vs. 0.68, P = 0.023).Conclusions: Both NCD and CCL predict motor outcome after resection of AVMs close to the CST. We propose the CLAD grading scale as an effective risk-prediction tool in surgical decision-making.Clinical Trial Registration:www.ClinicalTrials.gov, identifier: NCT01758211 and NCT02868008